‘Beyond nicotine’ marketing strategies: Big Tobacco diversification into the vaping and cannabis product sectors
Bibliographic record
Abstract
Tobacco sales in Canada and the USA have stagnated.1 2 Although denormalisation strategies have reframed smoking (and smokers) as socially unacceptable,3–6 these attitudes appear to apply uniquely to smoked tobacco. Cannabis now represents notable opportunities for tobacco companies, with recreational cannabis becoming legalised federally in Canada (effective October 2018) and increasingly at the state level in the USA. While the social acceptability of cigarette smoking has declined over time, cannabis use has become chic and desirable. For example, Barneys—a set of high-end US department stores that were known for selling designer handbags, shoes and clothing—offered a ‘Lifestyle Shop’ of luxury cannabis products and accessories in strategic efforts to appeal to ‘status seekers’.7 According to Barneys’ website, ‘It’s no secret that there has been a huge cultural shift when it comes to cannabis. What was once taboo is now being embraced as part of the wellness routine of a variety of lifestyles, and it’s led to a burgeoning new industry’.8 Not surprisingly, tobacco companies have invested in the nascent cannabis sector. Altria, for example, invested $2.4 billion to acquire a 45% ownership stake in the Canadian cannabis company, Cronos.9 British American Tobacco (BAT), meanwhile, acquired a nearly 20% stake in Organigram, which is also a Canada-based cannabis company. Cannabis is a sector where tobacco companies can leverage their compatible strengths, which include well-established supply chain and distribution channels, global reach, expertise regarding mergers and acquisitions, and experience with navigating within stringent regulatory environments (figure 1).10 According to David O’Reilly, BAT’s Director of Scientific Research, …
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.008 | 0.010 |
| Scholarly communication | 0.013 | 0.012 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.012 | 0.002 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".